# Script to run QAP
library(sna)
library(doParallel)
library(doRNG)

# Load the followers adjacency matrix
y <- readRDS("processed_data/mentions_adjacencyMatrix.Rds")
# Load the predicting matrix 
load("processed_data/QAP_predicting_matrices.RData")

diag(y) <- NA

y_v <- c(y)


Var.names <-c("State_Similarity",
              "Party_Similarity",
              "Chamber_Similarity",
              "Gender_Similarity",
              "Race_Similarity",
              "Difference_in_Legislatures_Profeshionalism",
              "Dem_Sender_Effect",
              "Rep_Sender_Effect",
              "House_Sender_Effect",
              "Female_Sender_Effect",
              "Profesh_Sender_Effect",
              "Black_Sender_Effect",
              "Latino_Sender_Effect",
              "Asian_Sender_Effect",
              "Mena_Sender_Effect",
              "Multi_Sender_Effect",
              "Native_Sender_Effect",
              "Democrat_Receiver_Effect",
              "Republican_Receiver_Effect",
              "House_Receiver_Effect",
              "Female_Receiver_Effect",
              "Profesh_Receiver_Effect",
              "Black_Receiver_Effect",
              "Latino_Receiver_Effect",
              "Asian_Receiver_Effect",
              "Mena_Receiver_Effect",
              "Multi_Receiver_Effect",
              "Native_Receiver_Effect",
              "Same_Party_Same_State",
              "Same_Chamber_Same_State",
              "Same_Gender_Same_State",
              "Same_Race_Same_State",
              "Contiguous_States")

for(i in 1:length(Var.names)){
  xi <- predicting_matrices[i,,]
  diag(xi) <- NA
  print(sum(is.na(xi)))
  if(i == 1){
    xdf <- cbind(c(xi))
  }
  if(i > 1){
    xdf <- cbind(xdf,cbind(c(xi)))
  }
}

send_id <- matrix(1:nrow(y),nrow(y),nrow(y))
rec_id <- t(send_id)
diag(send_id) <- NA
diag(rec_id) <- NA

xdf <- cbind(xdf,c(send_id),c(rec_id))

xdf <- data.frame(xdf)

names(xdf) <- c(Var.names,c("send_id","rec_id"))

xydf <- data.frame(y_v,xdf)

xynd <- na.omit(xydf)

covs <- paste(Var.names,collapse="+")

names(xynd)[1] <- "yij"

library(fastglm)
system.time(est0 <- fastglm(as.matrix(xynd[,Var.names]),xynd$yij,family=gaussian(),method=3))

mu <- est0$fitted.values
sig2 <- var(residuals(est0))

nnet_sim <- 50

set.seed(92011)

sim_amats <- list()
for(i in 1:nnet_sim){
  edgesi <- rnorm(length(mu),mu,sig2)
  amati <- y
  amati[,] <- 0
  diag(amati) <- NA
  amati[c(xynd$send_id,xynd$rec_id)] <- edgesi
  sim_amats[[i]] <- amati
  if(i/10==round(i/10)) print(i)
}

save(list="sim_amats",file="simulated_mention_networks.RData")
